This invention relates generally to apparatus and methods for decoding communication signals, and, more particularly, to decoding communication signals containing signal-dependent noise.
With the continuing evolution of computer systems, there is an increasing demand for greater storage density. For example, due to advances in technology, the areal densities of magnetic recording media have increased steadily over the past several years. To increase areal densities using longitudinal recording, as well as increase overall storage capacity of the media, the data bits are typically made smaller and put closer together on the magnetic media (e.g., the hard disc). However, there are limits to how small the data bits can be made. If a bit becomes too small, the magnetic energy holding the bit in place may also become so small that thermal energy can cause it to demagnetize. This phenomenon is known as superparamagnetism. To avoid superparamagnetic effects, magnetic media manufacturers have been increasing the coercivity (the “field” required to write a bit) of the media. However, the coercivity of the media is limited by the magnetic materials from which the write head is made.
In order to increase the areal densities of magnetic media even further, many media manufacturers are using perpendicular recording. Unlike traditional longitudinal recording, where the magnetization is lying in the plane of the magnetic medium, with perpendicular recording the media grains are oriented in the depth of the medium with their magnetization pointing either up or down, perpendicular to the plane of the disc. Using perpendicular recording, manufacturers have exceeded magnetic recording densities of 100 Gbits per square inch, and densities of 1 Terabit per square inch are feasible.
However, as storage densities increase, the signal processing of recording channels becomes more difficult. Sources of distortion, including media noise, electronics and head noise, signal transition noise (e.g., transition jitter), inter-track interference, thermal asperity, partial erasure, and dropouts, are becoming more and more pronounced. Particularly troublesome are signal-dependent types of noise, such as transition jitter, because these types of noise are quickly becoming the dominant sources of detection errors. Signal-dependent noise may overwhelm white noise and severely degrade the performance of detectors designed for a white noise channel.
There have been attempts to design detectors to mitigate signal-dependent noise. However, current detectors designed to combat signal-dependent noise have problems. One problem with these types of detectors is their complexity. In order to detect data in the presence of signal-dependent noise, these detectors employ complicated correction schemes, such as modifying the Euclidean branch metric to compensate for the signal-dependent noise or adaptively computing the branch metric. For example, some currently available detection techniques use either a non-linear Viterbi detector assuming the noise is Markov, or a non-linear post-processor with a similar Markov noise model. For a description of a non-linear post-processor with a Markov noise model, see U.S. Pat. No. 6,931,585, to Burd et al., which is hereby incorporated by reference herein in its entirety.
Another problem with current detection techniques is that they only perform well when the variance of the signal-dependent noise is small. This is because the first-order Taylor approximation of the noise process is only valid for small values of variance. Thus, when the signal-dependent noise variance is large, these detection techniques are far from optimal.
Thus, it would be desirable to provide a more robust post-processor architecture for correcting decision errors associated with signal-dependent sources of noise, such as transition jitter and pulse width noise. The post-processor may be used to output the final decision of a sub-optimal detector, such as a linear or non-linear Viterbi detector.